Senior AI/ML Engineer

Job Description: At Sparklight/Cableone and our family of brands, we keep our customers and associates connected to what matters most. For our associates, that means: a thriving and rewarding career, respect for the communities where they live and work, a focus on health and wellness, an excellent work/life balance, and an open and inclusive workplace.    We are open to hiring remote if we find the right talent in any of the following states: AL, AR, AZ,  FL, GA, IA, ID, IL, IN, KS, LA, MD, MO, MS, NC, ND, NE, NM, NV, OR, OK, PA, SC, SD, TN, TX, UT.   The Senior AI/ML Engineer will serve as the technical authority for AI/ML platforms, agent architecture, Model Context Protocol (MCP) strategy, context engineering, orchestration, governance, and AI-assisted experiences within Network Intelligence. This role will define how agentic systems safely consume network data, engineering knowledge, automation capabilities, and operational intelligence. The Senior AI/ML Engineer will establish reusable architectural patterns, development standards, evaluation practices, human approval controls, and governance requirements while providing technical mentorship to AI Engineers assigned to technology-domain delivery teams. The position will partner closely with the Senior Network Automation Engineer to maintain a clear boundary between deterministic network capability development and intelligent consumption of those capabilities. What you will do to contribute to the company’s success •  Define and own architecture and technical standards for AI/ML platforms, agent frameworks, agent harnesses, and agentic workflows. •  Define MCP strategy, server integration patterns, tool contracts, access controls, and lifecycle standards. •  Design reusable patterns for agent orchestration, multi-agent coordination, long-running workflows, and escalation paths. •  Establish standards for context engineering, memory systems, retrieval, grounding, source attribution, and knowledge packaging. •  Define human-in-the-loop approval requirements, reasoning boundaries, tool execution safeguards, auditability, and governance controls. •  Create evaluation frameworks and acceptance criteria for correctness, safety, reliability, hallucination reduction, and tool execution. •  Define how agents consume network APIs, automation services, data products, procedures, and engineering knowledge. •  Partner with the Senior Network Automation Engineer to maintain the capability contract between Network Automation Engineering and AI Engineering. •  Review complex, high-risk, or net-new AI and agentic solution designs. •  Guide AI Engineers assigned to technology-domain delivery teams and establish reusable implementation patterns. •  Provide technical mentorship, design guidance, code review, and architectural support for AI-focused engineering resources. •  Partner with Platform Engineering on AI service hosting, deployment, monitoring, alerting, scalability, and production readiness. •  Partner with Data Engineering, NMS Engineering, Reporting Engineering, and Capacity Engineering to ensure agents use trusted and appropriately structured data. •  Coordinate conversational and AI-assisted user experience requirements with UI/UX and front-end contributors. •  Produce High Level Designs (HLDs), architecture decision records, technical standards, and implementation guidance. •  Apply secure software development, CI/CD, source control, testing, and operational support practices to AI solutions. •  Evaluate emerging AI/ML, agentic, orchestration, and MCP technologies for practical enterprise adoption. •  Communicate architectural decisions, technical risks, dependencies, and recommendations to engineering and leadership stakeholders. Education and/or Experience ·       Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Data Science, Information Technology, or a related technical field is preferred. ·       Alternatively, 8 or more years of progressive experience in software engineering, platform engineering, data engineering, AI/ML engineering, or related technical disciplines will be considered. ·       Five or more years of experience designing or delivering AI/ML, large language model, or agentic systems is preferred. ·       Demonstrated experience leading technical architecture, establishing engineering standards, and guiding complex or net-new solution delivery. ·       Strong Python development skills and experience building production-grade services and integrations. ·       Experience with large language models, agent frameworks, tool calling, retrieval-augmented generation, context engineering, and model evaluation. ·       Experience designing MCP servers, MCP clients, or comparable tool-integration architectures is strongly preferred. ·       Experience with REST APIs, event-driven integrations, structured data, and enterprise system integration. ·       Experience with vector databases, graph databases, knowledge graphs, semantic retrieval, or metadata-driven knowledge systems. ·       Experience with cloud-based AI services, containerized deployment, Git, CI/CD, testing, monitoring, and production support. ·       Experience applying security, governance, human approval, auditability, and responsible AI practices to production systems. ·       Experience in telecommunications, ISP, network engineering, infrastructure, or operational technology environments is preferred. Certificates, Licenses, Registrations Specific Certifications are not required. There are a few that can demonstrate significant understanding of key concepts. ·       Microsoft Certified: Azure AI Engineer Associate ·       Microsoft Certified: Azure Solutions Architect Expert ·       Microsoft Certified: DevOps Engineer Expert ·       AWS Certified Machine Learning Engineer - Associate or AWS Certified Machine Learning - Specialty ·       Google Cloud Pro